Scaling single file snapshot performance across clustered system

ABSTRACT

In some embodiments, a process for restoring a version of a virtual machine using a data storage system comprises identifying a particular version of a virtual machine to be restored, determining a base image from which the particular version may be derived, determining a set of incremental files for generating the particular version, generating a file associated with the particular version using the base image and the set of incremental files, and outputting at least a portion of the file.

CLAIM OF PRIORITY

This application is a Continuation of U.S. application Ser. No.16/665,879, filed Oct. 28, 2019, which is hereby incorporated byreference in its entirety.

FIELD

The present disclosure relates generally to dynamically adjusting thepartitioning or sharding of large files to improve the snapshottingperformance of a distributed data management and storage system.

BACKGROUND

Virtualization allows virtual hardware to be created and decoupled fromthe underlying physical hardware. For example, a hypervisor running on ahost machine or server may be used to create one or more virtualmachines that may each run the same operating system or differentoperating systems (e.g., a first virtual machine may run a Windows®operating system and a second virtual machine may run a Unix-likeoperating system such as OS X®).

A virtual machine may comprise a software implementation of a physicalmachine. The virtual machine may include one or more virtual hardwaredevices, such as a virtual processor, a virtual memory, a virtual disk,or a virtual network interface card. The virtual machine may load andexecute an operating system and applications from the virtual memory.The operating system and applications executed by the virtual machinemay be stored using the virtual disk. The virtual machine may be stored(e.g., using a datastore comprising one or more physical storagedevices) as a set of files including a virtual disk file for storing thecontents of the virtual disk and a virtual machine configuration filefor storing configuration settings for the virtual machine. Theconfiguration settings may include the number of virtual processors(e.g., four virtual CPUs), the size of a virtual memory, and the size ofa virtual disk (e.g., a 2 TB virtual disk) for the virtual machine.

The present disclosure relates generally to dynamically adjusting thepartitioning or sharding of large files to improve the snapshottingperformance of a distributed data management and storage system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A depicts one embodiment of a networked computing environment.

FIG. 1B depicts one embodiment of a server.

FIG. 1C depicts one embodiment of a storage appliance.

FIG. 1D depicts one embodiment of a portion of an integrated datamanagement and storage system that includes a plurality of nodes incommunication with each other and one or more storage devices.

FIGS. 2A-2K depict various embodiments of sets of files and datastructures associated with managing and storing snapshots of virtualmachines.

FIG. 3A is a flowchart describing one embodiment of a process formanaging and storing virtual machine snapshots using a data storagesystem.

FIG. 3B is a flowchart describing one embodiment of a process fordetermining the type of snapshot to be stored using a data storagesystem.

FIG. 3C is a flowchart describing one embodiment of a process forrestoring a version of a virtual machine using a data storage system.

FIG. 4 illustrates operations in an example method 400 of managing afileset.

FIG. 5 depicts operations in an example method for fetching a singlelarge file before sharding in a single node of a cluster.

FIG. 6 depicts operations in an example method for sharding a singlelarge file and fetching it across nodes of a cluster.

DETAILED DESCRIPTION

Technology is described for dynamically adjusting the partitioning orsharding of large files to improve the snapshotting performance of adistributed data management and storage system. The distributed datamanagement and storage system may comprise a cluster of data storagenodes that acquires file metadata for a set of files corresponding witha point in time snapshot (e.g., a snapshot of one or more virtual disks)to be protected and identifies a first file within the set of files thathas a file size that exceeds a threshold file size (e.g., is greaterthan 100 GB or greater than 1 TB). The point in time snapshot maycomprise a snapshot of a virtual machine, a real machine, anapplication, a database, or a set of electronic files. The file metadatamay include the file size of the first file, file permissions (e.g.,read and write permissions), file type (e.g., is the file an executablefile), and time stamps associated with when the first file was firstcreated and last updated. The cluster may determine whether to acquireand store the first file as a single file or to partition the first fileand store the first file as a plurality of shards across the datastorage nodes based on the file size of the first file, the differencebetween the file size and the threshold file size, the total number ofdata storage nodes within the cluster, the speed of the data path fromthe source of the first file to the cluster, the number of data storagenodes within the cluster with at least a threshold amount of availabledisk space (e.g., with at least 500 GBs of available disk space), theestimated time to store the first file as a single file using thecluster, and/or the estimated time to archival of the first file inwhich the first file is transferred from the cluster of data storagenodes to an archival target (e.g., to a cloud-based archival target suchas Microsoft Azure® or Amazon Web Services®). The time to transfer thefirst file to the cluster may depend on the file sizes of the partitionsfor the first file that may be acquired in parallel and the datatransmission rate from the source of the first file.

Although a single node in the cluster of data storage nodes may besufficient to snapshot most filesets from various data sources, somesnapshots may require the cluster to acquire and store a very largeamount of data (e.g., 500 TBs of data). For these large snapshots, thecluster may utilize all of the data storage nodes within the clustersuch that snapshotting performance may be scaled as the number of datastorage nodes within the cluster grows. One issue with setting thesmallest unit of fileset partitioning as a single file is that thecluster cannot take advantage of an increase in the number of datastorage nodes. For example, if the cluster comprises an eight nodecluster that needs to store a 16 TB fileset with two 8 TB files, thenthe cluster would store the two files using only two nodes leaving theother six nodes unutilized. One technical benefit of partitioning largefiles into shards and fetching or pulling the shards across all the datastorage nodes of the cluster is that snapshotting performance may bescaled. For example, if the cluster comprises an eight-node cluster thatneeds to store a 16 TB fileset with two 8 TB files and sharding isperformed at a 1 TB granularity, then the cluster may fetch or pull theeight shards per file across the eight data storages nodes within thecluster thereby scaling the snapshotting performance based on number ofdata storage nodes within the cluster. A benefit derived from shardingthe files is that fetching or pulling multiple shards of a single fileacross multiple nodes is faster than just using one node. In fetch orpull examples, the data will be stored across all nodes in the cluster.In some examples, more nodes are used for performance.

The determination of when to partition a snapshot of files along onlyfile boundaries or when to allow large files of the snapshot that aregreater than a threshold file size to be partitioned and then distributethe partitions across the cluster of data storage nodes may depend onthe file sizes of the large files, the number of large files that aregreater than the threshold file size, the speed of the data path fromthe source to the cluster or an estimated time to acquire and store thelarge files a single files, and the amount of time until the files willbe archived or transferred to an archival target or the estimated timeto archival of the large files from the cluster of data storage nodes toan archival target. In some cases, prior to acquiring the first file,the distributed data management and storage system may acquire filemetadata for the set of files to identify a first subset of the set offiles that have file sizes that are greater than a first threshold filesize (e.g., are greater than 100 GB). The distributed data managementand storage system may then rank the first subset using the filemetadata, such as ranking the first subset of the set of files based ontheir file sizes or the estimated times to archival, and then identifyone or more files of the first subset to partition and store using aplurality of shards. In one example, the one or more files of the firstsubset may comprise the four files out of 10,000 files comprising asnapshot of a virtual disk that have a file size that is greater thanthe first threshold file size and the distributed data management andstorage system may rank the four files based on the longest estimatedtimes to archival. In this case, only two out of the four files thathave estimated times to archival greater than a threshold time toarchival (e.g., greater than one week) may be partitioned and storedusing a plurality of shards, while the other two files that haveestimated times to archival less than the threshold time to archival maybe stored as single files. The estimated times to archival may bedetermined based on requirements from SLA policies associated with theindividual files or with the snapshot of the virtual disk. For example,an SLA policy may specify that snapshots of particular files that areolder than ten days be moved to archival data storage. In some cases,all files of the first subset of the set of files that have file sizesthat are greater than a second threshold file size (e.g., are greaterthan 1 TB) greater than the first threshold file size may be partitionedand stored using a plurality of shards regardless of archivalrequirements for the files.

In some cases, a set of files (e.g., a fileset) may be partitionedlogically into a plurality of independently managed partitions or shardsand each partition may correspond with a separate chain. Each of thechains may include a full image snapshot (or a base image) and one ormore incremental snapshots (e.g., a forward incremental snapshot) thatderive from the full image snapshot. Each incremental snapshot maycomprise data changes relative to the full image snapshot. The datachanges may be represented using one or more changed data blocks. As thetime to restore a particular point in time version of the set of filesmay increase with the chain lengths for the chains (e.g., linearlyincreasing with the number of forward incremental snapshots per snapshotchain), additional full image snapshots may be acquired or generatedover time to limit the total number of incremental snapshots within thechains that need to be applied to generate the particular point in timeversion of the set of files.

An integrated data management and storage system may be configured tomanage the automated storage, backup, deduplication, replication,recovery, and archival of data within and across physical and virtualcomputing environments. The integrated data management and storagesystem may provide a unified primary and secondary storage system withbuilt-in data management that may be used as both a backup storagesystem and a “live” primary storage system for primary workloads. Insome cases, the integrated data management and storage system may managethe extraction and storage of historical snapshots associated withdifferent point in time versions of virtual machines and/or realmachines (e.g., a hardware server, a laptop, a tablet computer, asmartphone, or a mobile computing device) and provide near instantaneousrecovery of a backed-up version of a virtual machine, a real machine, orone or more files residing on the virtual machine or the real machine.The integrated data management and storage system may allow backed-upversions of real or virtual machines to be directly mounted or madeaccessible to primary workloads in order to enable the nearinstantaneous recovery of the backed-up versions and allow secondaryworkloads (e.g., workloads for experimental or analytics purposes) todirectly use the integrated data management and storage system as aprimary storage target to read or modify past versions of data.

The integrated data management and storage system may include adistributed cluster of storage nodes that presents itself as a unifiedstorage system even though numerous storage nodes may be connectedtogether and the number of connected storage nodes may change over timeas storage nodes are added to or removed from the cluster. Theintegrated data management and storage system may utilize a scale-outnode based architecture in which a plurality of data storage appliancescomprising one or more nodes are in communication with each other viaone or more networks. Each storage node may include two or moredifferent types of storage devices and control circuitry configured tostore, deduplicate, compress, and/or encrypt data stored using the twoor more different types of storage devices. In one example, a storagenode may include two solid-state drives (SSDs), three hard disk drives(HDDs), and one or more processors configured to concurrently read datafrom and/or write data to the storage devices. The integrated datamanagement and storage system may replicate and distribute versioneddata, metadata, and task execution across the distributed cluster toincrease tolerance to node and disk failures (e.g., snapshots of avirtual machine may be triply mirrored across the cluster). Datamanagement tasks may be assigned and executed across the distributedcluster in a fault tolerant manner based on the location of data withinthe cluster (e.g., assigning tasks to nodes that store data related tothe task) and node resource availability (e.g., assigning tasks to nodeswith sufficient compute or memory capacity for the task).

The integrated data management and storage system may apply a databackup and archiving schedule to backed-up real and virtual machines toenforce various backup service level agreements (SLAs), recovery pointobjectives (RPOs), recovery time objectives (RTOs), data retentionrequirements, and other data backup, replication, and archival policiesacross the entire data lifecycle. For example, the data backup andarchiving schedule may require that snapshots of a virtual machine arecaptured and stored every four hours for the past week, every day forthe past six months, and every week for the past five years.

As virtualization technologies are adopted into information technology(IT) infrastructures, there is a growing need for recovery mechanisms tosupport mission critical application deployment within a virtualizedinfrastructure. However, a virtualized infrastructure may present a newset of challenges to the traditional methods of data management due tothe higher workload consolidation and the need for instant, granularrecovery. The benefits of using an integrated data management andstorage system include the ability to reduce the amount of data storagerequired to backup real and virtual machines, the ability to reduce theamount of data storage required to support secondary or non-productionworkloads, the ability to provide a non-passive storage target in whichbackup data may be directly accessed and modified, and the ability toquickly restore earlier versions of virtual machines and files storedlocally or in the cloud.

FIG. 1A depicts one embodiment of a networked computing environment 100in which the disclosed technology may be practiced. As depicted, thenetworked computing environment 100 includes a data center 150, astorage appliance 140, and a computing device 154 in communication witheach other via one or more networks 180. The networked computingenvironment 100 may include a plurality of computing devicesinterconnected through one or more networks 180. The one or morenetworks 180 may allow computing devices and/or storage devices toconnect to and communicate with other computing devices and/or otherstorage devices. In some cases, the networked computing environment mayinclude other computing devices and/or other storage devices not shown.The other computing devices may include, for example, a mobile computingdevice, a non-mobile computing device, a server, a workstation, a laptopcomputer, a tablet computer, a desktop computer, or an informationprocessing system. The other storage devices may include, for example, astorage area network storage device, a networked-attached storagedevice, a hard disk drive, a solid-state drive, or a data storagesystem. The one or more networks 180 may include a cellular network, amobile network, a wireless network, a wired network, a secure networksuch as an enterprise private network, an unsecure network such as awireless open network, a local area network (LAN), a wide area network(WAN), and the Internet.

The data center 150 may include one or more servers, such as server 160,in communication with one or more storage devices, such as storagedevice 156. The one or more servers may also be in communication withone or more storage appliances, such as storage appliance 170. Theserver 160, storage device 156, and storage appliance 170 may be incommunication with each other via a networking fabric connecting serversand data storage units within the data center to each other. The server160 may comprise a production hardware server. The storage appliance 170may include a data management system for backing up virtual machines,real machines, virtual disks, real disks, and/or electronic files withinthe data center 150. The server 160 may be used to create and manage oneor more virtual machines associated with a virtualized infrastructure.The one or more virtual machines may run various applications, such as adatabase application or a web server. The storage device 156 may includeone or more hardware storage devices for storing data, such as a harddisk drive (HDD), a magnetic tape drive, a solid-state drive (SSD), astorage area network (SAN) storage device, or a networked-attachedstorage (NAS) device. In some cases, a data center, such as data center150, may include thousands of servers and/or data storage devices incommunication with each other. The data storage devices may comprise atiered data storage infrastructure (or a portion of a tiered datastorage infrastructure). The tiered data storage infrastructure mayallow for the movement of data across different tiers of a data storageinfrastructure between higher-cost, higher-performance storage devices(e.g., solid-state drives and hard disk drives) and relativelylower-cost, lower-performance storage devices (e.g., magnetic tapedrives).

A server, such as server 160, may allow a client to download informationor files (e.g., executable, text, application, audio, image, or videofiles) from the server or to perform a search query related toparticular information stored on the server. In some cases, a server mayact as an application server or a file server. In general, a server mayrefer to a hardware device that acts as the host in a client-serverrelationship or a software process that shares a resource with orperforms work for one or more clients. One embodiment of server 160includes a network interface 165, processor 166, memory 167, disk 168,and virtualization manager 169 all in communication with each other.Network interface 165 allows server 160 to connect to one or morenetworks 180. Network interface 165 may include a wireless networkinterface and/or a wired network interface. Processor 166 allows server160 to execute computer readable instructions stored in memory 167 inorder to perform processes described herein. Processor 166 may includeone or more processing units, such as one or more CPUs and/or one ormore GPUs. Memory 167 may comprise one or more types of memory (e.g.,RAM, SRAM, DRAM, EEPROM, Flash, etc.). Disk 168 may include a hard diskdrive and/or a solid-state drive. Memory 167 and disk 168 may comprisehardware storage devices.

The virtualization manager 169 may manage a virtualized infrastructureand perform management operations associated with the virtualizedinfrastructure. For example, the virtualization manager 169 may managethe provisioning of virtual machines running within the virtualizedinfrastructure and provide an interface to computing devices interactingwith the virtualized infrastructure. The virtualization manager 169 mayalso perform various virtual machine related tasks, such as cloningvirtual machines, creating new virtual machines, monitoring the state ofvirtual machines, moving virtual machines between physical hosts forload balancing purposes, and facilitating backups of virtual machines.

One embodiment of storage appliance 170 includes a network interface175, processor 176, memory 177, and disk 178 all in communication witheach other. Network interface 175 allows storage appliance 170 toconnect to one or more networks 180. Network interface 175 may include awireless network interface and/or a wired network interface. Processor176 allows storage appliance 170 to execute computer readableinstructions stored in memory 177 in order to perform processesdescribed herein. Processor 176 may include one or more processingunits, such as one or more CPUs and/or one or more GPUs. Memory 177 maycomprise one or more types of memory (e.g., RAM, SRAM, DRAM, EEPROM, NORFlash, NAND Flash, etc.). Disk 178 may include a hard disk drive and/ora solid-state drive. Memory 177 and disk 178 may comprise hardwarestorage devices.

In one embodiment, the storage appliance 170 may include four machines.Each of the four machines may include a multi-core CPU, 64 GB of RAM, a400 GB SSD, three 4 TB HDDs, and a network interface controller. In thiscase, the four machines may be in communication with the one or morenetworks 180 via the four network interface controllers. The fourmachines may comprise four nodes of a server cluster. The server clustermay comprise a set of physical machines that are connected together viaa network. The server cluster may be used for storing data associatedwith a plurality of virtual machines, such as backup data associatedwith different point in time versions of one or more virtual machines.

In another embodiment, the storage appliance 170 may comprise a virtualappliance that comprises four virtual machines. Each of the virtualmachines in the virtual appliance may have 64 GB of virtual memory, a 12TB virtual disk, and a virtual network interface controller. In thiscase, the four virtual machines may be in communication with the one ormore networks 180 via the four virtual network interface controllers.The four virtual machines may comprise four nodes of a virtual cluster.

The networked computing environment 100 may provide a cloud computingenvironment for one or more computing devices. In one embodiment, thenetworked computing environment 100 may include a virtualizedinfrastructure that provides software, data processing, and/or datastorage services to end users accessing the services via the networkedcomputing environment. In one example, networked computing environment100 may provide cloud-based work productivity or business relatedapplications to a computing device, such as computing device 154. Thecomputing device 154 may comprise a mobile computing device or a tabletcomputer. The storage appliance 140 may comprise a cloud-based datamanagement system for backing up virtual machines and/or files within avirtualized infrastructure, such as virtual machines running on server160 or files stored on server 160.

In some embodiments, the storage appliance 170 may manage the extractionand storage of virtual machine snapshots associated with different pointin time versions of one or more virtual machines running within the datacenter 150. A snapshot of a virtual machine may correspond with a stateof the virtual machine at a particular point in time. In some cases, thesnapshot may capture the state of various virtual machine settings andthe state of one or more virtual disks for the virtual machine. Inresponse to a restore command from the server 160, the storage appliance170 may restore a point in time version of a virtual machine or restorepoint in time versions of one or more files located on the virtualmachine and transmit the restored data to the server 160. In response toa mount command from the server 160, the storage appliance 170 may allowa point in time version of a virtual machine to be mounted and allow theserver 160 to read and/or modify data associated with the point in timeversion of the virtual machine. To improve storage density, the storageappliance 170 may deduplicate and compress data associated withdifferent versions of a virtual machine and/or deduplicate and compressdata associated with different virtual machines. To improve systemperformance, the storage appliance 170 may first store virtual machinesnapshots received from a virtualized environment in a cache, such as aflash-based cache. The cache may also store popular data or frequentlyaccessed data (e.g., based on a history of virtual machinerestorations), incremental files associated with commonly restoredvirtual machine versions, and current day incremental files orincremental files corresponding with snapshots captured within the past24 hours.

An incremental file may comprise a forward incremental file or a reverseincremental file. A forward incremental file may include a set of datarepresenting changes that have occurred since an earlier point in timesnapshot of a virtual machine. To generate a snapshot of the virtualmachine corresponding with a forward incremental file, the forwardincremental file may be combined with an earlier point in time snapshotof the virtual machine (e.g., the forward incremental file may becombined with the last full image of the virtual machine that wascaptured before the forward incremental was captured and any otherforward incremental files that were captured subsequent to the last fullimage and prior to the forward incremental file). A reverse incrementalfile may include a set of data representing changes from a later pointin time snapshot of a virtual machine. To generate a snapshot of thevirtual machine corresponding with a reverse incremental file, thereverse incremental file may be combined with a later point in timesnapshot of the virtual machine (e.g., the reverse incremental file maybe combined with the most recent snapshot of the virtual machine and anyother reverse incremental files that were captured prior to the mostrecent snapshot and subsequent to the reverse incremental file).

The storage appliance 170 may provide a user interface (e.g., aweb-based interface or a graphical user interface) that displays virtualmachine information, such as identifications of the virtual machinesprotected and the historical versions or time machine views for each ofthe virtual machines protected, and allows an end user to search,select, and control virtual machines managed by the storage appliance. Atime machine view of a virtual machine may include snapshots of thevirtual machine over a plurality of points in time. Each snapshot maycomprise the state of the virtual machine at a particular point in time.Each snapshot may correspond with a different version of the virtualmachine (e.g., Version 1 of a virtual machine may correspond with thestate of the virtual machine at a first point in time and Version 2 ofthe virtual machine may correspond with the state of the virtual machineat a second point in time subsequent to the first point in time).

FIG. 1B depicts one embodiment of server 160 in FIG. 1A. The server 160may comprise one server out of a plurality of servers that are networkedtogether within a data center. In one example, the plurality of serversmay be positioned within one or more server racks within the datacenter. As depicted, the server 160 includes hardware-level componentsand software-level components. The hardware-level components include oneor more processors 182, one or more memory 184, and one or more disks185. The software-level components include a hypervisor 186, avirtualized infrastructure manager 199, and one or more virtualmachines, such as virtual machine 198. The hypervisor 186 may comprise anative hypervisor or a hosted hypervisor. The hypervisor 186 may providea virtual operating platform for running one or more virtual machines,such as virtual machine 198. Virtual machine 198 includes a plurality ofvirtual hardware devices including a virtual processor 192, a virtualmemory 194, and a virtual disk 195. The virtual disk 195 may comprise afile stored within the one or more disks 185. In one example, a virtualmachine may include a plurality of virtual disks, with each virtual diskof the plurality of virtual disks associated with a different filestored on the one or more disks 185. Virtual machine 198 may include aguest operating system 196 that runs one or more applications, such asapplication 197. The virtualized infrastructure manager 199, which maycorrespond with the virtualization manager 169 in FIG. 1A, may run on avirtual machine or natively on the server 160. The virtualizedinfrastructure manager 199 may provide a centralized platform formanaging a virtualized infrastructure that includes a plurality ofvirtual machines.

In one embodiment, the server 160 may use the virtualized infrastructuremanager 199 to facilitate backups for a plurality of virtual machines(e.g., eight different virtual machines) running on the server 160. Eachvirtual machine running on the server 160 may run its own guestoperating system and its own set of applications. Each virtual machinerunning on the server 160 may store its own set of files using one ormore virtual disks associated with the virtual machine (e.g., eachvirtual machine may include two virtual disks that are used for storingdata associated with the virtual machine).

In one embodiment, a data management application running on a storageappliance, such as storage appliance 140 in FIG. 1A or storage appliance170 in FIG. 1A, may request a snapshot of a virtual machine running onserver 160. The snapshot of the virtual machine may be stored as one ormore files, with each file associated with a virtual disk of the virtualmachine. A snapshot of a virtual machine may correspond with a state ofthe virtual machine at a particular point in time. The particular pointin time may be associated with a time stamp. In one example, a firstsnapshot of a virtual machine may correspond with a first state of thevirtual machine (including the state of applications and files stored onthe virtual machine) at a first point in time (e.g., 6:30 p.m. on Jun.29, 2017) and a second snapshot of the virtual machine may correspondwith a second state of the virtual machine at a second point in timesubsequent to the first point in time (e.g., 6:30 p.m. on Jun. 30,2017).

In response to a request for a snapshot of a virtual machine at aparticular point in time, the virtualized infrastructure manager 199 mayset the virtual machine into a frozen state or store a copy of thevirtual machine at the particular point in time. The virtualizedinfrastructure manager 199 may then transfer data associated with thevirtual machine (e.g., an image of the virtual machine or a portion ofthe image of the virtual machine) to the storage appliance. The dataassociated with the virtual machine may include a set of files includinga virtual disk file storing contents of a virtual disk of the virtualmachine at the particular point in time and a virtual machineconfiguration file storing configuration settings for the virtualmachine at the particular point in time. The contents of the virtualdisk file may include the operating system used by the virtual machine,local applications stored on the virtual disk, and user files (e.g.,images and word processing documents). In some cases, the virtualizedinfrastructure manager 199 may transfer a full image of the virtualmachine to the storage appliance or a plurality of data blockscorresponding with the full image (e.g., to enable a full image-levelbackup of the virtual machine to be stored on the storage appliance). Inother cases, the virtualized infrastructure manager 199 may transfer aportion of an image of the virtual machine associated with data that haschanged since an earlier point in time prior to the particular point intime or since a last snapshot of the virtual machine was taken. In oneexample, the virtualized infrastructure manager 199 may transfer onlydata associated with changed blocks stored on a virtual disk of thevirtual machine that have changed since the last snapshot of the virtualmachine was taken. In one embodiment, the data management applicationmay specify a first point in time and a second point in time and thevirtualized infrastructure manager 199 may output one or more changeddata blocks associated with the virtual machine that have been modifiedbetween the first point in time and the second point in time.

FIG. 1C depicts one embodiment of a storage appliance, such as storageappliance 170 in FIG. 1A. The storage appliance may include a pluralityof physical machines that may be grouped together and presented as asingle computing system. Each physical machine of the plurality ofphysical machines may comprise a node in a cluster (e.g., a failovercluster). As depicted, the storage appliance 170 includes hardware-levelcomponents and software-level components. The hardware-level componentsinclude one or more physical machines, such as physical machine 120 andphysical machine 130. The physical machine 120 includes a networkinterface 121, processor 122, memory 123, and disk 124 all incommunication with each other. Processor 122 allows physical machine 120to execute computer readable instructions stored in memory 123 toperform processes described herein. Disk 124 may include a hard diskdrive and/or a solid-state drive. The physical machine 130 includes anetwork interface 131, processor 132, memory 133, and disk 134 all incommunication with each other. Processor 132 allows physical machine 130to execute computer readable instructions stored in memory 133 toperform processes described herein. Disk 134 may include a hard diskdrive and/or a solid-state drive. In some cases, disk 134 may include aflash-based SSD or a hybrid HDD/SSD drive. In one embodiment, thestorage appliance 170 may include a plurality of physical machinesarranged in a cluster (e.g., eight machines in a cluster). Each of theplurality of physical machines may include a plurality of multi-coreCPUs, 128 GB of RAM, a 500 GB SSD, four 4 TB HDDs, and a networkinterface controller.

As depicted in FIG. 1C, the software-level components of the storageappliance 170 may include data management system 102, a virtualizationinterface 104, a distributed job scheduler 108, a distributed metadatastore 110, a distributed file system 112, and one or more virtualmachine search indexes, such as virtual machine search index 106. In oneembodiment, the software-level components of the storage appliance 170may be run using a dedicated hardware-based appliance. In anotherembodiment, the software-level components of the storage appliance 170may be run from the cloud (e.g., the software-level components may beinstalled on a cloud service provider).

In some cases, the data storage across a plurality of nodes in a cluster(e.g., the data storage available from the one or more physicalmachines) may be aggregated and made available over a single file systemnamespace (e.g., /snapshots/). A directory for each virtual machineprotected using the storage appliance 170 may be created (e.g., thedirectory for Virtual Machine A may be /snapshots/VM_A). Snapshots andother data associated with a virtual machine may reside within thedirectory for the virtual machine. In one example, snapshots of avirtual machine may be stored in subdirectories of the directory (e.g.,a first snapshot of Virtual Machine A may reside in /snapshots/VM_A/s1/and a second snapshot of Virtual Machine A may reside in/snapshots/VM_A/s2/).

The distributed file system 112 may present itself as a single filesystem, in which as new physical machines or nodes are added to thestorage appliance 170, the cluster may automatically discover theadditional nodes and automatically increase the available capacity ofthe file system for storing files and other data. Each file stored inthe distributed file system 112 may be partitioned into one or morechunks. Each of the one or more chunks may be stored within thedistributed file system 112 as a separate file. The files stored withinthe distributed file system 112 may be replicated or mirrored over aplurality of physical machines, thereby creating a load-balanced andfault tolerant distributed file system. In one example, storageappliance 170 may include ten physical machines arranged as a failovercluster and a first file corresponding with a full-image snapshot of avirtual machine (e.g., /snapshots/VM_A/s1/s1.full) may be replicated andstored on three of the ten machines. In some cases, the data chunksassociated with a file stored in the distributed file system 112 mayinclude replicated data (e.g., due to n-way mirroring) or parity data(e.g., due to erasure coding). When a disk storing one of the datachunks fails, then the distributed file system may regenerate the lostdata and store the lost data using a new disk.

In one embodiment, the distributed file system 112 may be used to storea set of versioned files corresponding with a virtual machine. The setof versioned files may include a first file comprising a full image ofthe virtual machine at a first point in time and a second filecomprising an incremental file relative to the full image. The set ofversioned files may correspond with a snapshot chain for the virtualmachine. The distributed file system 112 may determine a first set ofdata chunks that includes redundant information for the first file(e.g., via application of erasure code techniques) and store the firstset of data chunks across a plurality of nodes within a cluster. Theplacement of the first set of data chunks within the cluster may bedetermined based on the locations of other data related to the first setof data chunks (e.g., the locations of other chunks corresponding withthe second file or other files within the snapshot chain for the virtualmachine). In some embodiments, the distributed file system 112 may alsoco-locate data chunks or replicas of virtual machines discovered to besimilar to each other in order to allow for cross virtual machinededuplication. In this case, the placement of the first set of datachunks may be determined based on the locations of other datacorresponding with a different virtual machine that has been determinedto be sufficiently similar to the virtual machine.

The distributed metadata store 110 may comprise a distributed databasemanagement system that provides high availability without a single pointof failure. The distributed metadata store 110 may act as a quick-accessdatabase for various components in the software stack of the storageappliance 170 and may store metadata corresponding with stored snapshotsusing a solid-state storage device, such as a solid-state drive (SSD) ora Flash-based storage device. In one embodiment, the distributedmetadata store 110 may comprise a database, such as a distributeddocument oriented database. The distributed metadata store 110 may beused as a distributed key value storage system. In one example, thedistributed metadata store 110 may comprise a distributed NoSQL keyvalue store database. In some cases, the distributed metadata store 110may include a partitioned row store, in which rows are organized intotables or other collections of related data held within a structuredformat within the key value store database. A table (or a set of tables)may be used to store metadata information associated with one or morefiles stored within the distributed file system 112. The metadatainformation may include the name of a file, a size of the file, filepermissions associated with the file, when the file was last modified,and file mapping information associated with an identification of thelocation of the file stored within a cluster of physical machines. Inone embodiment, a new file corresponding with a snapshot of a virtualmachine may be stored within the distributed file system 112 andmetadata associated with the new file may be stored within thedistributed metadata store 110. The distributed metadata store 110 mayalso be used to store a backup schedule for the virtual machine and alist of snapshots for the virtual machine that are stored using thestorage appliance 170.

In some cases, the distributed metadata store 110 may be used to manageone or more versions of a virtual machine. The concepts described hereinmay also be applicable to managing versions of a real machine orversions of electronic files. Each version of the virtual machine maycorrespond with a full image snapshot of the virtual machine storedwithin the distributed file system 112 or an incremental snapshot of thevirtual machine (e.g., a forward incremental or reverse incremental)stored within the distributed file system 112. In one embodiment, theone or more versions of the virtual machine may correspond with aplurality of files. The plurality of files may include a single fullimage snapshot of the virtual machine and one or more incrementalsderived from the single full image snapshot. The single full imagesnapshot of the virtual machine may be stored using a first storagedevice of a first type (e.g., a HDD) and the one or more incrementalsderived from the single full image snapshot may be stored using a secondstorage device of a second type (e.g., an SSD). In this case, only asingle full image needs to be stored and each version of the virtualmachine may be generated from the single full image or the single fullimage combined with a subset of the one or more incrementals.Furthermore, each version of the virtual machine may be generated byperforming a sequential read from the first storage device (e.g.,reading a single file from an HDD) to acquire the full image and, inparallel, performing one or more reads from the second storage device(e.g., performing fast random reads from an SSD) to acquire the one ormore incrementals. In some cases, a first version of a virtual machinecorresponding with a first snapshot of the virtual machine at a firstpoint in time may be generated by concurrently reading a full image forthe virtual machine corresponding with a state of the virtual machineprior to the first point in time from the first storage device whilereading one or more incrementals from the second storage devicedifferent from the first storage device (e.g., reading the full imagefrom a HDD at the same time as reading 64 incrementals from an SSD).

The distributed job scheduler 108 may comprise a distributed faulttolerant job scheduler, in which jobs affected by node failures arerecovered and rescheduled to be run on available nodes. In oneembodiment, the distributed job scheduler 108 may be fully decentralizedand implemented without the existence of a master node. The distributedjob scheduler 108 may run job scheduling processes on each node in acluster or on a plurality of nodes in the cluster and each node mayindependently determine which tasks to execute. The distributed jobscheduler 108 may be used for scheduling backup jobs that acquire andstore virtual machine snapshots for one or more virtual machines overtime. The distributed job scheduler 108 may follow a backup schedule tobackup an entire image of a virtual machine at a particular point intime or one or more virtual disks associated with the virtual machine atthe particular point in time.

The job scheduling processes running on at least a plurality of nodes ina cluster (e.g., on each available node in the cluster) may manage thescheduling and execution of a plurality of jobs. The job schedulingprocesses may include run processes for running jobs, cleanup processesfor cleaning up failed tasks, and rollback processes for rolling-back orundoing any actions or tasks performed by failed jobs. In oneembodiment, the job scheduling processes may detect that a particulartask for a particular job has failed and in response may perform acleanup process to clean up or remove the effects of the particular taskand then perform a rollback process that processes one or more completedtasks for the particular job in reverse order to undo the effects of theone or more completed tasks. Once the particular job with the failedtask has been undone, the job scheduling processes may restart theparticular job on an available node in the cluster.

The virtualization interface 104 may provide an interface forcommunicating with a virtualized infrastructure manager managing avirtualized infrastructure, such as the virtualized infrastructuremanager 199 in FIG. 1B, and for requesting data associated with virtualmachine snapshots from the virtualized infrastructure. Thevirtualization interface 104 may communicate with the virtualizedinfrastructure manager using an API for accessing the virtualizedinfrastructure manager (e.g., to communicate a request for a snapshot ofa virtual machine).

The virtual machine search index 106 may include a list of files thathave been stored using a virtual machine and a version history for eachof the files in the list. Each version of a file may be mapped to theearliest point in time snapshot of the virtual machine that includes theversion of the file or to a snapshot of the virtual machine thatincludes the version of the file (e.g., the latest point in timesnapshot of the virtual machine that includes the version of the file).In one example, the virtual machine search index 106 may be used toidentify a version of the virtual machine that includes a particularversion of a file (e.g., a particular version of a database, aspreadsheet, or a word processing document). In some cases, each of thevirtual machines that are backed up or protected using storage appliance170 may have a corresponding virtual machine search index.

The data management system 102 may comprise an application running onthe storage appliance that manages the capturing, storing,deduplication, compression (e.g., using a lossless data compressionalgorithm such as LZ4 or LZ77), and encryption (e.g., using a symmetrickey algorithm such as Triple DES or AES-256) of data for the storageappliance 170. In one example, the data management system 102 maycomprise a highest level layer in an integrated software stack runningon the storage appliance. The integrated software stack may include thedata management system 102, the virtualization interface 104, thedistributed job scheduler 108, the distributed metadata store 110, andthe distributed file system 112. In some cases, the integrated softwarestack may run on other computing devices, such as a server or computingdevice 154 in FIG. 1A. The data management system 102 may use thevirtualization interface 104, the distributed job scheduler 108, thedistributed metadata store 110, and the distributed file system 112 tomanage and store one or more snapshots of a virtual machine. Eachsnapshot of the virtual machine may correspond with a point in timeversion of the virtual machine. The data management system 102 maygenerate and manage a list of versions for the virtual machine. Eachversion of the virtual machine may map to or reference one or morechunks and/or one or more files stored within the distributed filesystem 112. Combined together, the one or more chunks and/or the one ormore files stored within the distributed file system 112 may comprise afull image of the version of the virtual machine.

In some embodiments, a plurality of versions of a virtual machine may bestored as a base file associated with a complete image of the virtualmachine at a particular point in time and one or more incremental filesassociated with forward and/or reverse incremental changes derived fromthe base file. The data management system 102 may patch together thebase file and the one or more incremental files in order to generate aparticular version of the plurality of versions by adding and/orsubtracting data associated with the one or more incremental files fromthe base file or intermediary files derived from the base file. In someembodiments, each version of the plurality of versions of a virtualmachine may correspond with a merged file. A merged file may includepointers or references to one or more files and/or one or more chunksassociated with a particular version of a virtual machine. In oneexample, a merged file may include a first pointer or symbolic link to abase file and a second pointer or symbolic link to an incremental fileassociated with the particular version of the virtual machine. In someembodiments, the one or more incremental files may correspond withforward incrementals (e.g., positive deltas), reverse incrementals(e.g., negative deltas), or a combination of both forward incrementalsand reverse incrementals.

FIG. 1D depicts one embodiment of a portion of an integrated datamanagement and storage system that includes a plurality of nodes incommunication with each other and one or more storage devices via one ormore networks 180. The plurality of nodes may be networked together andpresent themselves as a unified storage system. The plurality of nodesincludes node 141 and node 147. The one or more storage devices includestorage device 157 and storage device 158. Storage device 157 maycorrespond with a cloud-based storage (e.g., private or public cloudstorage). Storage device 158 may comprise a hard disk drive (HDD), amagnetic tape drive, a solid-state drive (SSD), a storage area network(SAN) storage device, or a networked-attached storage (NAS) device. Theintegrated data management and storage system may comprise a distributedcluster of storage appliances in which each of the storage appliancesincludes one or more nodes. In one embodiment, node 141 and node 147 maycomprise two nodes housed within a first storage appliance, such asstorage appliance 170 in FIG. 1C. In another embodiment, node 141 maycomprise a first node housed within a first storage appliance and node147 may comprise a second node housed within a second storage appliancedifferent from the first storage appliance. The first storage applianceand the second storage appliance may be located within a data center,such as data center 150 in FIG. 1A, or located within different datacenters.

As depicted, node 141 includes a network interface 142, a nodecontroller 143, and a first plurality of storage devices including HDDs144-145 and SSD 146. The first plurality of storage devices may comprisetwo or more different types of storage devices. The node controller 143may comprise one or more processors configured to store, deduplicate,compress, and/or encrypt data stored within the first plurality ofstorage devices. Node 147 includes a network interface 148, a nodecontroller 149, and a second plurality of storage devices including HDDs151-152 and SSD 153. The second plurality of storage devices maycomprise two or more different types of storage devices. The nodecontroller 149 may comprise one or more processors configured to store,deduplicate, compress, and/or encrypt data stored within the secondplurality of storage devices. In some cases, node 141 may correspondwith physical machine 120 in FIG. 1C and node 147 may correspond withphysical machine 130 in FIG. 1C.

FIGS. 2A-2K depict various embodiments of sets of files and datastructures (e.g., implemented using merged files) associated withmanaging and storing snapshots of virtual machines. Although variousembodiments may be described in reference to the management of virtualmachine snapshots, the concepts may be applied to the management ofother data snapshots as well, such as snapshots of databases, filesets(e.g., Network Attached Storage filesets), and sets of electronic files.

FIG. 2A depicts one embodiment of a set of virtual machine snapshotsstored as a first set of files. The first set of files may be storedusing a distributed file system, such as distributed file system 112 inFIG. 1C. As depicted, the first set of files includes a set of reverseincrementals (R1-R4), a full image (Base), and a set of forwardincrementals (F1-F2). The set of virtual machine snapshots includesdifferent versions of a virtual machine (versions V1-V7 of VirtualMachine A) captured at different points in time (times T1-T7). In somecases, the file size of the reverse incremental R3 and the file size ofthe forward incremental F2 may both be less than the file size of thebase image corresponding with version V5 of Virtual Machine A. The baseimage corresponding with version V5 of Virtual Machine A may comprise afull image of Virtual Machine A at point in time T5. The base image mayinclude a virtual disk file for Virtual Machine A at point in time T5.The reverse incremental R3 corresponds with version V2 of VirtualMachine A and the forward incremental F2 corresponds with version V7 ofVirtual Machine A. The forward incremental F1 may be associated with thedata changes that occurred to Virtual Machine A between time T5 and timeT6 and may comprise one or more changed data blocks.

In some embodiments, each snapshot of the set of virtual machinesnapshots may be stored within a storage appliance, such as storageappliance 170 in FIG. 1A. In other embodiments, a first set of the setof virtual machine snapshots may be stored within a first storageappliance and a second set of the set of virtual machine snapshots maybe stored within a second storage appliance, such as storage appliance140 in FIG. 1A. In this case, a data management system may extend acrossboth the first storage appliance and the second storage appliance. Inone example, the first set of the set of virtual machine snapshots maybe stored within a local cluster repository (e.g., recent snapshots ofthe file may be located within a first data center) and the second setof the set of virtual machine snapshots may be stored within a remotecluster repository (e.g., older snapshots or archived snapshots of thefile may be located within a second data center) or a cloud repository.

FIG. 2B depicts one embodiment of a merged file for generating versionV7 of Virtual Machine A using the first set of files depicted in FIG.2A. The merged file includes a first pointer (pBase) that references thebase image Base (e.g., via the path/snapshots/VM_A/s5/s5.full), a secondpointer (pF1) that references the forward incremental F1 (e.g., via thepath/snapshots/VM_A/s6/s6.delta), and a third pointer (pF2) thatreferences the forward incremental F2 (e.g., via thepath/snapshots/VM_A/s7/s7.delta). In one embodiment, to generate thefull image of version V7 of Virtual Machine A, the base image may beacquired, the data changes associated with forward incremental F1 may beapplied to (or patched to) the base image to generate an intermediateimage, and then the data changes associated with forward incremental F2may be applied to the intermediate image to generate the full image ofversion V7 of Virtual Machine A.

FIG. 2C depicts one embodiment of a merged file for generating versionV2 of Virtual Machine A using the first set of files depicted in FIG.2A. The merged file includes a first pointer (pBase) that references thebase image Base (e.g., via the path/snapshots/VM_A/s5/s5.full), a secondpointer (pR1) that references the reverse incremental R1 (e.g., via thepath/snapshots/VM_A/s4/s4.delta), a third pointer (pR2) that referencesthe reverse incremental R2 (e.g., via thepath/snapshots/VM_A/s3/s3.delta), and a fourth pointer (pR3) thatreferences the reverse incremental R3 (e.g., via thepath/snapshots/VM_A/s2/s2.delta). In one embodiment, to generate thefull image of version V2 of Virtual Machine A, the base image may beacquired, the data changes associated with reverse incremental R1 may beapplied to the base image to generate a first intermediate image, thedata changes associated with reverse incremental R2 may be applied tothe first intermediate image to generate a second intermediate image,and then the data changes associated with reverse incremental R3 may beapplied to the second intermediate image to generate the full image ofversion V2 of Virtual Machine A.

FIG. 2D depicts one embodiment of a set of virtual machine snapshotsstored as a second set of files after a rebasing process has beenperformed using the first set of files in FIG. 2A. The second set offiles may be stored using a distributed file system, such as distributedfile system 112 in FIG. 1C. The rebasing process may generate new filesR12, R11, and Base2 associated with versions V5-V7 of Virtual Machine Ain order to move a full image closer to a more recent version of VirtualMachine A and to improve the reconstruction time for the more recentversions of Virtual Machine A. The data associated with the full imageBase in FIG. 2A may be equivalent to the new file R12 patched over R11and the full image Base2. Similarly, the data associated with the fullimage Base2 may be equivalent to the forward incremental F2 in FIG. 2Apatched over F1 and the full image Base in FIG. 2A.

The process of moving the full image snapshot for the set of virtualmachine snapshots to correspond with the most recent snapshot versionmay be performed in order to shorten or reduce the chain lengths for thenewest or most recent snapshots, which may comprise the snapshots ofVirtual Machine A that are the most likely to be accessed. In somecases, a rebasing operation (e.g., that moves the full image snapshotfor a set of virtual machine snapshots to correspond with the mostrecent snapshot version) may be triggered when a number of forwardincremental files is greater than a threshold number of forwardincremental files for a snapshot chain (e.g., more than 200 forwardincremental files). In other cases, a rebasing operation may betriggered when the total disk size for the forward incremental filesexceeds a threshold disk size (e.g., is greater than 200 GB) or isgreater than a threshold percentage (e.g., is greater than 20%) of thebase image for the snapshot chain.

In some cases, the rebasing process may be part of a periodic rebasingprocess that is applied at a rebasing frequency (e.g., every 24 hours)to each virtual machine of a plurality of protected virtual machines toreduce the number of forward incremental files that need to be patchedto a base image in order to restore the most recent version of a virtualmachine. Periodically reducing the number of forward incremental filesmay reduce the time to restore the most recent version of the virtualmachine as the number of forward incremental files that need to beapplied to a base image to generate the most recent version may belimited. In one example, if a rebasing process is applied to snapshotsof a virtual machine every 24 hours and snapshots of the virtual machineare acquired every four hours, then the number of forward incrementalfiles may be limited to at most five forward incremental files.

As depicted, the second set of files includes a set of reverseincrementals (R11-R12 and R1-R4) and a full image (Base2). The set ofvirtual machine snapshots includes the different versions of the virtualmachine (versions V1-V7 of Virtual Machine A) captured at the differentpoints in time (times T1-T7) depicted in FIG. 2A. In some cases, thefile size of the reverse incremental R2 may be substantially less thanthe file size of the base image Base2. The reverse incremental R2corresponds with version V2 of Virtual Machine A and the base imageBase2 corresponds with version V7 of Virtual Machine A. In this case,the most recent version of Virtual Machine A (i.e., the most recentrestore point for Virtual Machine A) comprises a full image. To generateearlier versions of Virtual Machine A, reverse incrementals may beapplied to (or patched to) the full image Base2. Subsequent versions ofVirtual Machine A may be stored as forward incrementals that depend fromthe full image Base2.

In one embodiment, a rebasing process may be applied to a first set offiles associated with a virtual machine in order to generate a secondset of files to replace the first set of files. The first set of filesmay include a first base image from which a first version of the virtualmachine may be derived and a first forward incremental file from which asecond version of the virtual machine may be derived. The second set offiles may include a second reverse incremental file from which the firstversion of the virtual machine may be derived and a second base imagefrom which the second version of the virtual machine may be derived.During the rebasing process, data integrity checking may be performed todetect and correct data errors in the files stored in a file system,such as distributed file system 112 in FIG. 1C, that are read togenerate the second set of files.

FIG. 2E depicts one embodiment of a merged file for generating versionV7 of Virtual Machine A using the second set of files depicted in FIG.2D. The merged file includes a first pointer (pBase2) that referencesthe base image Base2 (e.g., via the path/snapshots/VM_A/s7/s7.full). Inthis case, the full image of version V7 of Virtual Machine A may bedirectly acquired without patching forward incrementals or reverseincrementals to the base image Base2 corresponding with version V7 ofVirtual Machine A.

FIG. 2F depicts one embodiment of a merged file for generating versionV2 of Virtual Machine A using the second set of files depicted in FIG.2D. The merged file includes a first pointer (pBase2) that referencesthe base image Base2 (e.g., via the path/snapshots/VM_A/s7/s7.full), asecond pointer (pRI1) that references the reverse incremental R11 (e.g.,via the path/snapshots/VM_A/s6/s6.delta), a third pointer (pR12) thatreferences the reverse incremental R12 (e.g., via thepath/snapshots/VM_A/s5/s5.delta), a fourth pointer (pR1) that referencesthe reverse incremental R1 (e.g., via thepath/snapshots/VM_A/s4/s4.delta), a fifth pointer (pR2) that referencesthe reverse incremental R2 (e.g., via thepath/snapshots/VM_A/s3/s3.delta), and a sixth pointer (pR3) thatreferences the reverse incremental R3 (e.g., via thepath/snapshots/VM_A/s2/s2.delta). In one embodiment, to generate thefull image of version V2 of Virtual Machine A, the base image may beacquired, the data changes associated with reverse incremental R11 maybe applied to the base image to generate a first intermediate image, thedata changes associated with reverse incremental R12 may be applied tothe first intermediate image to generate a second intermediate image,the data changes associated with reverse incremental R1 may be appliedto the second intermediate image to generate a third intermediate image,the data changes associated with reverse incremental R2 may be appliedto the third intermediate image to generate a fourth intermediate image,and then the data changes associated with reverse incremental R3 may beapplied to the fourth intermediate image to generate the full image ofversion V2 of Virtual Machine A.

FIG. 2G depicts one embodiment of a set of files associated withmultiple virtual machine snapshots. The set of files may be stored usinga distributed file system, such as distributed file system 112 in FIG.1C. As depicted, the set of files includes a second full image (BaseB),a set of forward incrementals (F1-F2 and F5-F6) that derive from thesecond full image (BaseB), and a set of reverse incrementals (R1-R3)that derive from the second full image (BaseB). The set of files alsoincludes a first full image (BaseA) and a reverse incremental (R4) thatderives from the first full image (BaseA). In this case, the depictedsnapshots for Virtual Machine A include two different full imagesnapshots (BaseA and BaseB). Each of the full image snapshots maycomprise an anchor snapshot for a snapshot chain. The first full image(BaseA) and the reverse incremental (R4) may comprise a first snapshotchain with the first full image acting as the anchor snapshot. A secondsnapshot chain may comprise the second full image (BaseB) acting as theanchor snapshot for the second snapshot chain, the set of forwardincrementals (F1-F2), and the set of reverse incrementals (R1-R3). Thefirst snapshot chain and the second snapshot chain may be independent ofeach other and independently managed. For example, the base imageassociated with the second snapshot chain for Virtual Machine A may berepositioned (e.g., via rebasing) without impacting the first snapshotchain for Virtual Machine A.

A third snapshot chain for Virtual Machine C may comprise the secondfull image (BaseB) and forward incrementals (F1 and F5-F6). The firstsnapshot chain for Virtual Machine A and the third snapshot chain forVirtual Machine C may be independent of each other and independentlymanaged. However, as Virtual Machine C is a dependent virtual machinethat depends from the second snapshot chain for Virtual Machine A,changes to the second snapshot chain may impact the third snapshotchain. For example, repositioning of the base image for the secondsnapshot chain due to rebasing may require the merged files for thethird snapshot chain to be updated.

In some embodiments, each of the snapshot chains for Virtual Machine Amay have a maximum incremental chain length (e.g., no more than 100total incremental files), a maximum reverse incremental chain length(e.g., no more than 50 reverse incremental files), and a maximum forwardincremental chain length (e.g., no more than 70 forward incrementalfiles. In the event that a new snapshot will cause one of the snapshotchains to violate the maximum incremental chain length, the maximumreverse incremental chain length, or the maximum forward incrementalchain length, then a new snapshot chain may be created for VirtualMachine A and a new full-image base file may be stored for the newsnapshot chain.

FIG. 2H depicts one embodiment of a merged file for generating versionVS of Virtual Machine A using the set of files depicted in FIG. 2G. Themerged file includes a first pointer (pBaseA) that references the firstbase image BaseA and a second pointer (pR4) that references the reverseincremental R4. In one embodiment, to generate the full image of versionVS of Virtual Machine A, the first base image associated with Version VTof Virtual Machine A may be acquired and the data changes associatedwith reverse incremental R4 may be applied to the first base image togenerate the full image of version VS of Virtual Machine A.

FIG. 2I depicts one embodiment of a merged file for generating versionVU of Virtual Machine A using the set of files depicted in FIG. 2G. Themerged file includes a first pointer (pBaseB) that references the secondbase image BaseB, a second pointer (pR1) that references the reverseincremental R1, a third pointer (pR2) that references the reverseincremental R2, and a fourth pointer (pR3) that references the reverseincremental R3. In one embodiment, to generate the full image of versionVU of Virtual Machine A, the second base image associated with VersionVY of Virtual Machine A may be acquired, the data changes associatedwith reverse incremental R1 may be applied to the second base image togenerate a first intermediate image, the data changes associated withreverse incremental R2 may be applied to the first intermediate image togenerate a second intermediate image, and the data changes associatedwith reverse incremental R3 may be applied to the second intermediateimage to generate the full image of version VU of Virtual Machine A.

FIG. 2J depicts one embodiment of a set of files associated withmultiple virtual machine snapshots after a rebasing process has beenperformed to a snapshot chain using the set of files in FIG. 2G. The setof files may be stored using a distributed file system, such asdistributed file system 112 in FIG. 1C. The rebasing process maygenerate new files R12, R11, and BaseB2. As depicted, the set of filesincludes a set of reverse incrementals (R11-R12 and R1-R2), a full image(BaseB2), and a set of forward incrementals (F5-F7). In this case, asecond version of Virtual Machine C may be generated using forwardincrementals F5-F6 that are derived from Version VZ of Virtual MachineA. Forward incremental file F7 may include changes to Version VW ofVirtual Machine A that occurred subsequent to the generation of the fullimage file BaseB2. In some cases, the forward incremental file F7 maycomprise a writeable file or have file permissions allowing modificationof the file, while all other files associated with earlier versions ofVirtual Machine A comprise read only files.

FIG. 2K depicts one embodiment of a merged file for generating versionVU of Virtual Machine A using the set of files depicted in FIG. 2J. Themerged file includes a first pointer (pBaseA) that references the firstbase image BaseA and a second pointer (pF9) that references the forwardincremental F9. In one embodiment, to generate the full image of versionVU of Virtual Machine A, the first base image associated with Version VTof Virtual Machine A may be acquired and the data changes associatedwith forward incremental F9 may be applied to the first base image togenerate the full image of version VU of Virtual Machine A.

In some embodiments, upon detection that a second snapshot chain hasreached a maximum incremental chain length (e.g., no more than 500 totalincremental files), a maximum reverse incremental chain length (e.g., nomore than 400 reverse incremental files), or a maximum forwardincremental chain length (e.g., no more than 150 forward incrementalfiles), an existing snapshot chain (e.g., the first snapshot chaindepicted in FIG. 2J) may have its chain length extended or snapshotspreviously assigned to the second snapshot chain may be moved to theexisting snapshot chain. For example, the first snapshot chain depictedin FIG. 2G comprises two total snapshots, while the first snapshot chaindepicted in FIG. 2J comprises three total snapshots as the snapshotcorresponding with version VU of Virtual Machine A has moved from thesecond snapshot chain to the first snapshot chain.

In some embodiments, the number of snapshots in a snapshot chain maydecrease over time as older versions of a virtual machine areconsolidated, archived, deleted, or moved to a different storage domain(e.g., to cloud storage) depending on the data backup and archivingschedule for the virtual machine. In some cases, the maximum incrementalchain length or the maximum number of snapshots for a snapshot chain maybe increased over time as the versions stored by the snapshot chain age.In one example, if the versions of a virtual machine stored using asnapshot chain are all less than one month old, then the maximumincremental chain length may be set to a maximum of 200 incrementals;however, if the versions of the virtual machine stored using thesnapshot chain are all greater than one month old, then the maximumincremental chain length may be set to a maximum of 1000 incrementals.

In some embodiments, the maximum incremental chain length for a snapshotchain may be increased over time as the number of allowed snapshots in asnapshot chain may be increased as the backed-up versions of a virtualmachine get older. For example, the maximum incremental chain length fora snapshot chain storing versions of a virtual machine that are lessthan one year old may comprise a maximum incremental chain length of 200incrementals, while the maximum incremental chain length for a snapshotchain storing versions of a virtual machine that are more than one yearold may comprise a maximum incremental chain length of 500 incrementals.

FIG. 3A is a flowchart describing one embodiment of a process formanaging and storing virtual machine snapshots using a data storagesystem. In one embodiment, the process of FIG. 3A may be performed by astorage appliance, such as storage appliance 170 in FIG. 1A.

In step 302, a schedule for backing up a first virtual machine isdetermined. In one example, the schedule for backing up the firstvirtual machine may comprise periodically backing up the first virtualmachine every four hours. The schedule for backing up the first virtualmachine may be derived from a new backup, replication, and archivalpolicy or backup class assigned to the first virtual machine. In step304, a job scheduler is configured to implement the schedule for backingup the first virtual machine. In one example, a distributed jobscheduler, such as distributed job scheduler 108 in FIG. 1C, may beconfigured to schedule and run processes for capturing and storingimages of the first virtual machine over time according the schedule. Instep 306, a snapshot process for acquiring a snapshot of the firstvirtual machine is initiated. The snapshot process may send aninstruction to a virtualized infrastructure manager, such asvirtualization manager 169 in FIG. 1A, that requests data associatedwith the snapshot of the first virtual machine. In step 308, a type ofsnapshot to be stored is determined. The type of snapshot may comprise afull image snapshot or an incremental snapshot. In some cases, a fullimage snapshot may be captured and stored in order to serve as an anchorsnapshot for a new snapshot chain. Versions of the first virtual machinemay be stored using one or more independent snapshot chains, whereineach snapshot chain comprises a full image snapshot and one or moreincremental snapshots. One embodiment of a process for determining thetype of snapshot to be stored (e.g., storing either a full imagesnapshot or an incremental snapshot) is described later in reference toFIG. 3B.

In step 310, it is determined whether a full image of the first virtualmachine needs to be stored in order to store the snapshot of the firstvirtual machine. The determination of whether a full image is requiredmay depend on whether a previous full image associated with a priorversion of the first virtual machine has been acquired. Thedetermination of whether a full image is required may depend on thedetermination of the type of snapshot to be stored in step 308. If afull image needs to be stored, then step 311 is performed. Otherwise, ifa full image does not need to be stored, then step 312 is performed. Instep 311, the full image of the first virtual machine is acquired. Thefull image of the first virtual machine may correspond with a file orone or more data chunks. In step 312, changes relative to a priorversion of the first virtual machine or relative to another virtualmachine (e.g., in the case that the first virtual machine comprises adependent virtual machine whose snapshots derive from a full imagesnapshot of a second virtual machine different from the first virtualmachine) are acquired. The changes relative to the prior version of thefirst virtual machine or relative to a version of a different virtualmachine may correspond with a file or one or more data chunks. In step313, the full image of the first virtual machine is stored using adistributed file system, such as distributed file system 112 in FIG. 1C.In step 314, the changes relative to the prior version of the firstvirtual machine or relative to another virtual machine are stored usinga distributed file system, such as distributed file system 112 in FIG.1C. In one embodiment, the full image of the first virtual machine maybe stored using a first storage device of a first type (e.g., an HDD)and the changes relative to the prior version of the first virtualmachine may be stored using a second storage device of a second type(e.g., an SSD).

In some embodiments, snapshots of the first virtual machine may beingested at a snapshot capture frequency (e.g., every 30 minutes) by adata storage system. When a snapshot of the first virtual machine isingested, the snapshot may be compared with other snapshots storedwithin the data storage system in order to identify a candidate snapshotfrom which the snapshot may depend. In one example, a scalableapproximate matching algorithm may be used to identify the candidatesnapshot whose data most closely matches the data associated with thesnapshot or to identify the candidate snapshot whose data has the fewestnumber of data differences with the snapshot. In another example, anapproximate matching algorithm may be used to identify the candidatesnapshot whose data within a first portion of the candidate snapshotmost closely matches data associated with a first portion of thesnapshot. In some cases, a majority of the data associated with thesnapshot and the candidate snapshot may be identical (e.g., both thesnapshot and the candidate snapshot may be associated with virtualmachines that use the same operating system and have the sameapplications installed). Once the candidate snapshot has beenidentified, then data differences (or the delta) between the snapshotand the candidate snapshot may be determined and the snapshot may bestored based on the data differences. In one example, the snapshot maybe stored using a forward incremental file that includes the datadifferences between the snapshot and the candidate snapshot. The forwardincremental file may be compressed prior to being stored within a filesystem, such as distributed file system 112 in FIG. 1C.

In step 316, a merged file associated with the snapshot is generated.The merged file may reference one or more files or one or more datachunks that have been acquired in either step 311 or step 312. In oneexample, the merged file may comprise a file or a portion of a file thatincludes pointers to the one or more files or the one or more datachunks. In step 318, the merged file is stored in a metadata store, suchas distributed metadata store 110 in FIG. 1C. In step 320, a virtualmachine search index for the first virtual machine is updated. Thevirtual machine search index for the first virtual machine may include alist of files that have been stored in the first virtual machine and aversion history for each of the files in the list. In one example, thevirtual machine search index for the first virtual machine may beupdated to include new files that have been added to the first virtualmachine since a prior snapshot of the first virtual machine was takenand/or to include updated versions of files that were previously storedin the first virtual machine.

FIG. 3B is a flowchart describing one embodiment of a process fordetermining the type of snapshot to be stored using a data storagesystem. The process described in FIG. 3B is one example of a process forimplementing step 308 in FIG. 3A. In one embodiment, the process of FIG.3B may be performed by a storage appliance, such as storage appliance170 in FIG. 1A.

In step 332, a snapshot chain for a first virtual machine is identified.The snapshot chain may comprise a full image snapshot for the firstvirtual machine and one or more incremental snapshots that derive fromthe full image snapshot. Backed-up versions of the first virtual machinemay correspond with one or more snapshot chains. Each of the one or moresnapshot chains may include a full image snapshot or a base image fromwhich incremental snapshots may derive.

In step 334, it is determined whether the snapshot chain includes adependent base file. In this case, the first virtual machine maycomprise a dependent virtual machine that has snapshots that derive froma full image snapshot of a different virtual machine. In one embodiment,the first virtual machine and the different virtual machine from whichthe first virtual machine depends may each have different virtualmachine configuration files for storing configuration settings for thevirtual machines. In one example, the first virtual machine may have afirst number of virtual processors (e.g., two processors) and thedifferent virtual machine may have a second number of virtual processorsdifferent from the first number of virtual processors (e.g., fourprocessors). In another example, the first virtual machine may have afirst virtual memory size (e.g., 1 GB) and the different virtual machinemay have a second virtual memory size different from the first virtualmemory size (e.g., 2 GB). In another example, the first virtual machinemay run a first guest operating system and the different virtual machinemay run a second guest operating system different from the first guestoperating system.

In step 336, a maximum incremental chain length for the snapshot chainis determined based on whether the snapshot chain includes a dependentbase file. In one example, if the first virtual machine comprises adependent virtual machine, then the maximum incremental chain length maybe set to a maximum length of 200 snapshots; however if the firstvirtual machine is independent and is not a dependent virtual machine,then the maximum incremental chain length may be set to a maximum lengthof 500 snapshots.

In one embodiment, the maximum incremental chain length for the snapshotchain may be determined based on an age of the backed-up versions withinthe snapshot chain. In one example, the maximum incremental chain lengthfor a snapshot chain storing versions of the first virtual machine thatare less than one year old may comprise a maximum incremental chainlength of 100 incrementals, while the maximum incremental chain lengthfor a snapshot chain storing versions of the first virtual machine thatare more than one year old may comprise a maximum incremental chainlength of 200 incrementals.

In step 338, it is determined whether a new snapshot chain should becreated based on the maximum incremental chain length. In step 340, atype of snapshot to be stored for the first virtual machine isdetermined based on the maximum incremental chain length. The type ofsnapshot may comprise either a full image snapshot or an incrementalsnapshot. In one embodiment, if the snapshot chain for the first virtualmachine exceeds the maximum incremental chain length for the snapshotchain, then the type of snapshot to be stored for the first virtualmachine may comprise a full image snapshot. In this case, an additionalsnapshot chain may be created for the first virtual machine.

In some embodiments, the number of snapshots in a snapshot chain maydecrease over time as older versions of a virtual machine areconsolidated, archived, deleted, or moved to a different storage domain(e.g., to cloud storage) depending on the data backup and archivingschedule for the virtual machine. In some cases, the maximum incrementalchain length or the maximum number of snapshots for a snapshot chain maybe increased over time as the versions stored by the snapshot chain age.In one example, if the versions of a virtual machine stored using asnapshot chain are all less than one month old, then the maximumincremental chain length may be set to a maximum of 200 incrementals;however, if the versions of the virtual machine stored using thesnapshot chain are all greater than one month old, then the maximumincremental chain length may be set to a maximum of 1000 incrementals.

FIG. 3C is a flowchart describing one embodiment of a process forrestoring a version of a virtual machine using a data storage system. Inone embodiment, the process of FIG. 3C may be performed by a storageappliance, such as storage appliance 170 in FIG. 1A.

In step 382, a particular version of a virtual machine to be restored isidentified. In step 384, a base image from which the particular versionmay be derived is determined. In step 386, a set of incremental filesfor generating the particular version is determined. In one embodiment,the base image and the set of incremental files may be determined from amerged file associated with the particular version of the virtualmachine. In some cases, the set of incremental files may include one ormore forward incremental files and/or one or more reverse incrementalfiles. In step 388, a file associated with the particular version isgenerated using the base image and the set of incremental files. Thefile may be generated by patching the set of incremental files onto thebase image. In step 390, at least a portion of the file is outputted.The at least a portion of the file may be electronically transferred toa computing device, such as computing device 154 in FIG. 1A, or to avirtualization manager, such as virtualization manager 169 in FIG. 1A.

In some embodiments, a distributed cloud data management (CDM) platformmanages different data sources like VM, Oracle, SQL Server, Fileset, andso forth. The data sources may be referred to as snappables. While asingle node in a cluster is enough to snapshot many of the data sources,some snappables like Fileset especially NAS may include hundreds ofterra-bytes (TB) of data. In these cases, all the nodes of a cluster maybe employed to snapshot huge data sets to facilitate scaling upsnapshotting performance as further nodes are added to the cluster.Fileset partitioning may help the distribution of a large data setacross the nodes of a cluster and hence scale the snapshot performancewith more nodes in the cluster. However, in some instances the smallestunit of fileset partitioning may be a file. If a fileset is composed ofsingle or few large files, scaled performance may not necessarily beinvoked as the number of nodes in the cluster is scaled up. For example,if an example has an eight-node cluster and a 16 TB fileset with two 8TB files, a fileset partitioning would distribute the fileset into twonodes leaving 6 nodes unutilized. The same might be the case if anexample attempted to restore the same fileset. Some conventionalexamples do not scale a backup or restore performance as clusters arescaled up for massive files. The present disclosure aims at addressingthe problem by sharding large files and distributing them across thenodes.

Thus, in some embodiments, assume the same fileset example describedabove. If an example shards at 1 TB granularity, a 16 TB fileset can besharded into 8 shards for the first file and 8 for the second file. Ifan example runs two shards in parallel in a single node, all the 16shards can be run in parallel in the 8 node cluster thereby scaling theperformance based on number of nodes in the cluster.

By way of background to some examples, a filesystem may include orconstitute a collection of mutable files organized in a hierarchicalstructure, accompanied by metadata which describe file properties suchpermissions and access times. Filesystems may be used to store allmanner of data, like operating systems, applications, and user homedirectories containing user documents and other data. It is anabstraction of block-level storage by the operating system to expose itin a way that can be used by software applications. When backing up afilesystem, an aim is to store a complete or partial set of the files onthe filesystem. One conventional method is to create a copy of thesource data on to the target (backup data store). The backup store andor target may be managed by or through a distributed data managementplatform, for example.

The time to take a filesystem backup may be dependent on the size of thedata source and a maximum speed of the data path from the source to thetarget. For example,

T=(source size)/min(hop₁,hop₂, . . . ,hop_(n)), where

T is the time to take a backup

source size is a size of filesystem

hop_(n)—transmission speed between layers

Traditionally, backup data is transmitted from the source to a singlebackup server and then to a disk or disk array. This can present twobottlenecks, namely the CPU speed when hashing is involved, and afilesystem input/output (I/O) speed depending on the type of filesystemused. Some examples include an aggregation of multiple nodes withcompute and storage capabilities. In some present examples, a fileset ispartitioned based on logical size and distributed across the nodes of acluster. FIG. 4 illustrates operations in an example method 400 ofmanaging a fileset. In some examples, method 400 may include processfetching metadata for a filesystem by performing a metadata scan. Apartitioning algorithm may be run to shard the fileset based on themetadata obtained in the above step. The files may be grouped intopartitions based on the logical size and shard size. For example, if anexample has 100 files in filesystem each at 1 GB in logical size and apartition size of 10 GB, this will be split into 10 partitions each with10 files. For each of the partitions, one or multiple SDFS volumes arecreated, scaling linearly with the fileset partition size based and SDFSvolume size. These are aggregated in some examples into a single-uselarge volume SDFS volume aggregator layer, for example a SplitMJF layer.In some examples, a loop device is mounted on top of Split MJF layer asshown in FIG. 4. In a further example operation, an Ext4 filesystem iscreated on the loop device. Data from the data source may bere-synchronized and mounted. However, in some examples, in the operationabove in which the fileset is partitioned, the smallest boundary is asingle file. If an example has a fileset with one large single file,this cannot be partitioned conventionally and hence it cannot bedistributed across nodes in a cluster. Thus, some examples hereindisclose a method for sharding such a single large file into multiplepartitions so that it can be distributed across nodes in a cluster.

FIG. 5 depicts operations showing how a single large file is fetchedbefore sharding in a single node of a cluster. In some examples, themethod 500 includes scanning metadata for a fileset. The filesetmetadata may be stored in a filesystem metadata (FMD) file. In someexamples, partitioning is performed based on information in the FMDfile. Each entry in the FMD file may be defined by an 1 node datastructure. Some examples include an optional field to the 1 nodeinformation stored in an FMD file for each file in the fileset. A shardinfo application may denote if this file is a complete file or one ofthe shards of a sharded file. In some examples, the shard info app isconfigured to select or operate only for a file or files that aresharded into small files to be distributed across nodes during a fetchand restore operation. For example, an is_shard_file query will be falsefor an actual large file and true for all shards of the actual largefile.

Reference has been made above to partitioning a fileset based onmetadata scanning. In some present examples, during partitioning, for afile which is smaller than a file level sharding size, the shard infoapp is not triggered or implemented. For one or more files that aregreater than the relevant file level sharding size, these are shardedinto multiple shards and each of the shard files forms a partition.Shard file names may be framed in a certain way in some examples, withspecial characters to denote values being out of a valid ascii range(for example) to avoid a collision with actual file names. A shard filename may also include or have appended to it a random hash as an extrasafety precaution to avoid collision. The shard info app may beconfigured and set for the actual file and the shard files accordingly.

The example described above may invoke very few changes to pre-existingpartitioning logic as partitioning code can scan and FMD file as is doneconventionally. Since additional entries are added to an FMD file, thereis a possibility of file name collision but this can be handled asdescribed above. Moreover, additional entries in an FMD file which arespecific to filesets may be ignored in browse and search indexes.

Various operations in an example method 600 showing how a single file issharded and fetched across nodes of cluster is illustrated in FIG. 6.Once the partitioning operations have been performed based on file levelsharding (for example as described above), one or more fetch operationsmay proceed. If the fetched partitions in a node include a sharded file,in some examples fetch logic understands the shard file namingconvention (for example, as discussed above), and fetches the datasource. In some examples, the fetch logic determines an offset rangefrom the source file that belongs to the shard based on a shard info appconfiguration. Table 1 below provides some examples:

Shard File Name Actual file to fetch from source large_file~1_defglarge_file xlargefile~2_dceb xlargefile myfiledb~6_dfdb myfiledb

When these shard files are created, for example, on the EXT4 filesystemdiscussed above, the special out of range ASCII character will betranslated into a valid character that can be used as a functional filename. This value may be translated back during a restore operation.

In some examples, sharded files can be fetched in the same way as normalfiles save for the following changes. A bulk file reader may be adjustedto read an offset for the source data based on a shard size and shardnumber. When adding a file extension to read, a new destination offsetmaybe added to the extension to denote a source offset to read from theshard file. For example if this shard size is 500 GB, and the shardnumber is three, and actual source offset to be read from four andoffset of 100 GB of this shard file will be (500×3)+100 equals 1600 GB.A local file writer may write to the correct shard file offset insteadof a source file offset.

Since partitioning is performed with a sharded file, these files can befetched in parallel across different nodes of a cluster. Table 2 belowincludes some examples of a shard offset and actual file offsetsassuming a shard size of 500 GB:

Shard Number Shard File Offset Offset from actual source 0 200 GB 200 GB5 100 GB 2600 GB 8 350 GB 4350 GB

With regard to restoring operations, if the file that is being restoredis a sharded file, this can be identified from the FMD file and therestoring operation can fetch all the shard files corresponding to thefile. Restore operations of all these shard files for a given file canoccur in parallel across the nodes of a cluster, in some examples. Sincethe offset ranges do not overlap, the restore operations occurring inparallel across the nodes can write to the same file in the restoredestination.

In some examples relating to downloading sharded files, restoring adirectory does not invoke extra changes apart from changes for restoringa single file as a petitioning API would return all the partitions torestore including partitions for the sharded file. In some examples, afileset download API performs at least some of the following operations.An example operation may include identification of one or morepartitions to restore for a given set of files or directories todownload. A further operation may include creating a zip file for eachof the partitions with the restored file. A further operation mayinclude performing a zip merge for all the zip files for individualfiles.

In some examples of method 600, instead of modifying an FMD file toshard a file and store information about sharding, a fileset partitionfile is modified to do the same. In some examples, a partition file mayinclude a list of paths separated by a comma. Each partition may containall the files between two entries in the partition file. Some examplesmodify this approach such that each of the entries becomes a recordwhich can hold information about the shards. Each record may include apartition start path and an optional start offset and size for a shardfile. If the offset and size values are set, this partition stalls theoffset ranges for the first file of the partition. All other files ofthe partition, if any, will be complete. Fileset code bases may bemodified in some examples to identify whether a partition file is in alegacy or new format. Thus, in some examples of method 600, the shardingchange is isolated to a fileset code base and is rendered independent ofa common FMD format.

Some present examples include fileset fingerprinting in sharding. A filestate may maintain fingerprints for each of the files in the fileset.This may be used to fetch only changed blocks in a file that is modifiedbetween incremental snapshots. This may be useful in host-based filesetsin which network traffic can be reduced by simply fetching the changedrocks in a large file. For each fileset, an example may maintain onefingerprint for each 64 K block. Fingerprint files may be maintainedonly for the latest snapshot. In some examples, a fingerprint metadatafile may be established for each partition and there may only be onefingerprint data file which holds the actual fingerprints. Thefingerprint metadata file name that maps a file name to an offset valuewhere the fingerprints data for that file is found in the fingerprintdata file. For example, assuming two instances having a shard size of500 GB, and a fingerprint set at an offset 100, data at offset 1100 GBat the source would be relevant to obtain the changed data blocks.

Thus, some examples herein allow a fileset system to shard a large fileinto multiple shards. In some examples, the sharded files do not affectbrowse or search indexes. In some examples the sharded files belong todifferent partitions and can be fetched in parallel across nodes similarto partitions. Restore operations function normally, namely a file levelrestore, a folder level restore, and a fileset level restore. Snapshotand/or restore scaling is rendered possible based on cluster size andmay be performed at an enterprise scale. In conventional examples, alarge file may affect the overall snapshot or restore time because itcannot be distributed across a cluster. The present disclosure addressesthis problem.

The disclosed technology may be described in the context ofcomputer-executable instructions, such as software or program modules,being executed by a computer or processor. The computer-executableinstructions may comprise portions of computer program code, routines,programs, objects, software components, data structures, or other typesof computer-related structures that may be used to perform processesusing a computer. In some cases, hardware or combinations of hardwareand software may be substituted for software or used in place ofsoftware.

Computer program code used for implementing various operations oraspects of the disclosed technology may be developed using one or moreprogramming languages, including an object oriented programming languagesuch as Java or C++, a function programming language such as Scala, aprocedural programming language such as the “C” programming language orVisual Basic, or a dynamic programming language such as Python orJavaScript. In some cases, computer program code or machine-levelinstructions derived from the computer program code may execute entirelyon an end user's computer, partly on an end user's computer, partly onan end user's computer and partly on a remote computer, or entirely on aremote computer or server.

For purposes of this document, it should be noted that the dimensions ofthe various features depicted in the Figures may not necessarily bedrawn to scale.

For purposes of this document, reference in the specification to “anembodiment,” “one embodiment,” “some embodiments,” or “anotherembodiment” may be used to describe different embodiments and do notnecessarily refer to the same embodiment.

For purposes of this document, a connection may be a direct connectionor an indirect connection (e.g., via another part). In some cases, whenan element is referred to as being connected or coupled to anotherelement, the element may be directly connected to the other element orindirectly connected to the other element via intervening elements. Whenan element is referred to as being directly connected to anotherelement, then there are no intervening elements between the element andthe other element.

For purposes of this document, the term “based on” may be read as “basedat least in part on.”

For purposes of this document, without additional context, use ofnumerical terms such as a “first” object, a “second” object, and a“third” object may not imply an ordering of objects, but may instead beused for identification purposes to identify different objects.

For purposes of this document, the term “set” of objects may refer to a“set” of one or more of the objects.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A method comprising: sharding a file in afilesystem into a plurality of shard files based on filesystem metadata;partitioning the filesystem into a plurality of partitions based on theplurality of shard files, each shard file, of the plurality of shardfiles, corresponding to a partition, of the plurality of partitions; anddistributing each shard file, in the plurality of shard files, to arespective node, the distributing of the plurality of shard files beingrelated to the plurality of partitions.
 2. The method of claim 1,wherein the plurality of partitions includes a first partition and asecond partition, wherein the plurality of shard files includes a firstshard file and a second shard file, wherein the distributing includesdistributing the first shard file to the first partition of thefilesystem and distributing the second shard file to the secondpartition of the filesystem.
 3. The method of claim 1, wherein shardingthe file into the plurality of shard files includes creating theplurality of shard files on a fourth extended (EXT4) filesystem under aLinux operating system.
 4. The method of claim 1, wherein each of thepartitions, in the plurality of partitions, respectively corresponds toa shard file in the plurality of shards.
 5. The method of claim 1,further comprising scanning at least one file in the filesystem togenerate the filesystem metadata, wherein the scanning is performed bypartitioning logic.
 6. The method of claim 1, wherein the plurality ofshard files includes a first shard file and wherein the first shard fileis appended with a random hash.
 7. A system comprising: a memory storinginstructions; one or more processors configured by the instructions toperform operations in a method, the operations comprising at least:sharding a file in a filesystem into a plurality of shard files based onfilesystem metadata; partitioning the filesystem into a plurality ofpartitions based on the plurality of shard files, each shard file, ofthe plurality of shard files, corresponding to a partition, of theplurality of partitions; and distributing each shard file, in theplurality of shard files, to a respective node, the distributing of theplurality of shard files being related to the plurality of partitions.8. The system of claim 7, wherein the plurality of partitions includes afirst partition and a second partition, wherein the plurality of shardfiles includes a first shard file and a second shard file, wherein thedistributing includes distributing the first shard file to the firstpartition of the filesystem and distributing the second shard file tothe second partition of the filesystem.
 9. The system of claim 7,wherein sharding the file into the plurality of shard files includescreating the plurality of shard files on a fourth extended (EXT4)filesystem under a Linux operating system.
 10. The system of claim 7,wherein each of the partitions, in the plurality of partitions,respectively corresponds to a shard file, in the plurality of shards.11. The system of claim 7, wherein the operations further comprisescanning at least one file in the filesystem to generate the filesystemmetadata, wherein the scanning is performed by partitioning logic. 12.The system of claim 7, wherein the plurality of shard files includes afirst shard file and wherein the first shard file is appended with arandom hash.
 13. A non-transitory machine-readable medium comprisinginstructions which, when read by a machine, cause the machine to performoperations in a method, the operations comprising at least: sharding afile in a filesystem into a plurality of shard files based on filesystemmetadata; partitioning the filesystem into a plurality of partitionsbased on the plurality of shard files, each shard file, of the pluralityof shard files, corresponding to a partition, of the plurality ofpartitions; and distributing each shard file, in the plurality of shardfiles, to a respective node, the distributing of the plurality of shardfiles being related to the plurality of partitions.
 14. The medium ofclaim 13, wherein the plurality of partitions includes a first partitionand a second partition, wherein the plurality of shard files includes afirst shard file and a second shard file, wherein the distributingincludes distributing the first shard file to the first partition of thefilesystem and distributing the second shard file to the secondpartition of the filesystem.
 15. The medium of claim 13, whereinsharding the file into the plurality of shard files includes creatingthe plurality of shard files on a fourth extended (EXT4) filesystemunder a Linux operating system.
 16. The medium of claim 13, wherein eachof the partitions in the plurality of partitions respectivelycorresponds to a shard file in the plurality of shards.
 17. The mediumof claim 13, wherein the operations further comprise scanning at leastone file in the filesystem to generate the filesystem metadata, whereinthe scanning is performed by partitioning logic.
 18. The medium of claim13, wherein the plurality of shard files includes a first shard file andwherein the first shard file is appended with a random hash.